// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2016
// Mehdi Goli    Codeplay Software Ltd.
// Ralph Potter  Codeplay Software Ltd.
// Luke Iwanski  Codeplay Software Ltd.
// Contact: <eigen@codeplay.com>
// Benoit Steiner <benoit.steiner.goog@gmail.com>
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.

#define EIGEN_TEST_NO_LONGDOUBLE
#define EIGEN_TEST_NO_COMPLEX

#define EIGEN_DEFAULT_DENSE_INDEX_TYPE int64_t
#define EIGEN_USE_SYCL

#include "main.h"
#include <unsupported/Eigen/CXX11/Tensor>

using Eigen::array;
using Eigen::SyclDevice;
using Eigen::Tensor;
using Eigen::TensorMap;

template<typename DataType, int DataLayout, typename IndexType>
static void
test_simple_padding(const Eigen::SyclDevice& sycl_device)
{

	IndexType sizeDim1 = 2;
	IndexType sizeDim2 = 3;
	IndexType sizeDim3 = 5;
	IndexType sizeDim4 = 7;
	array<IndexType, 4> tensorRange = { { sizeDim1, sizeDim2, sizeDim3, sizeDim4 } };

	Tensor<DataType, 4, DataLayout, IndexType> tensor(tensorRange);
	tensor.setRandom();

	array<std::pair<IndexType, IndexType>, 4> paddings;
	paddings[0] = std::make_pair(0, 0);
	paddings[1] = std::make_pair(2, 1);
	paddings[2] = std::make_pair(3, 4);
	paddings[3] = std::make_pair(0, 0);

	IndexType padedSizeDim1 = 2;
	IndexType padedSizeDim2 = 6;
	IndexType padedSizeDim3 = 12;
	IndexType padedSizeDim4 = 7;
	array<IndexType, 4> padedtensorRange = { { padedSizeDim1, padedSizeDim2, padedSizeDim3, padedSizeDim4 } };

	Tensor<DataType, 4, DataLayout, IndexType> padded(padedtensorRange);

	DataType* gpu_data1 = static_cast<DataType*>(sycl_device.allocate(tensor.size() * sizeof(DataType)));
	DataType* gpu_data2 = static_cast<DataType*>(sycl_device.allocate(padded.size() * sizeof(DataType)));
	TensorMap<Tensor<DataType, 4, DataLayout, IndexType>> gpu1(gpu_data1, tensorRange);
	TensorMap<Tensor<DataType, 4, DataLayout, IndexType>> gpu2(gpu_data2, padedtensorRange);

	VERIFY_IS_EQUAL(padded.dimension(0), 2 + 0);
	VERIFY_IS_EQUAL(padded.dimension(1), 3 + 3);
	VERIFY_IS_EQUAL(padded.dimension(2), 5 + 7);
	VERIFY_IS_EQUAL(padded.dimension(3), 7 + 0);
	sycl_device.memcpyHostToDevice(gpu_data1, tensor.data(), (tensor.size()) * sizeof(DataType));
	gpu2.device(sycl_device) = gpu1.pad(paddings);
	sycl_device.memcpyDeviceToHost(padded.data(), gpu_data2, (padded.size()) * sizeof(DataType));
	for (IndexType i = 0; i < padedSizeDim1; ++i) {
		for (IndexType j = 0; j < padedSizeDim2; ++j) {
			for (IndexType k = 0; k < padedSizeDim3; ++k) {
				for (IndexType l = 0; l < padedSizeDim4; ++l) {
					if (j >= 2 && j < 5 && k >= 3 && k < 8) {
						VERIFY_IS_EQUAL(padded(i, j, k, l), tensor(i, j - 2, k - 3, l));
					} else {
						VERIFY_IS_EQUAL(padded(i, j, k, l), 0.0f);
					}
				}
			}
		}
	}
	sycl_device.deallocate(gpu_data1);
	sycl_device.deallocate(gpu_data2);
}

template<typename DataType, int DataLayout, typename IndexType>
static void
test_padded_expr(const Eigen::SyclDevice& sycl_device)
{
	IndexType sizeDim1 = 2;
	IndexType sizeDim2 = 3;
	IndexType sizeDim3 = 5;
	IndexType sizeDim4 = 7;
	array<IndexType, 4> tensorRange = { { sizeDim1, sizeDim2, sizeDim3, sizeDim4 } };

	Tensor<DataType, 4, DataLayout, IndexType> tensor(tensorRange);
	tensor.setRandom();

	array<std::pair<IndexType, IndexType>, 4> paddings;
	paddings[0] = std::make_pair(0, 0);
	paddings[1] = std::make_pair(2, 1);
	paddings[2] = std::make_pair(3, 4);
	paddings[3] = std::make_pair(0, 0);

	Eigen::DSizes<IndexType, 2> reshape_dims;
	reshape_dims[0] = 12;
	reshape_dims[1] = 84;

	Tensor<DataType, 2, DataLayout, IndexType> result(reshape_dims);

	DataType* gpu_data1 = static_cast<DataType*>(sycl_device.allocate(tensor.size() * sizeof(DataType)));
	DataType* gpu_data2 = static_cast<DataType*>(sycl_device.allocate(result.size() * sizeof(DataType)));
	TensorMap<Tensor<DataType, 4, DataLayout, IndexType>> gpu1(gpu_data1, tensorRange);
	TensorMap<Tensor<DataType, 2, DataLayout, IndexType>> gpu2(gpu_data2, reshape_dims);

	sycl_device.memcpyHostToDevice(gpu_data1, tensor.data(), (tensor.size()) * sizeof(DataType));
	gpu2.device(sycl_device) = gpu1.pad(paddings).reshape(reshape_dims);
	sycl_device.memcpyDeviceToHost(result.data(), gpu_data2, (result.size()) * sizeof(DataType));

	for (IndexType i = 0; i < 2; ++i) {
		for (IndexType j = 0; j < 6; ++j) {
			for (IndexType k = 0; k < 12; ++k) {
				for (IndexType l = 0; l < 7; ++l) {
					const float result_value =
						DataLayout == ColMajor ? result(i + 2 * j, k + 12 * l) : result(j + 6 * i, l + 7 * k);
					if (j >= 2 && j < 5 && k >= 3 && k < 8) {
						VERIFY_IS_EQUAL(result_value, tensor(i, j - 2, k - 3, l));
					} else {
						VERIFY_IS_EQUAL(result_value, 0.0f);
					}
				}
			}
		}
	}
	sycl_device.deallocate(gpu_data1);
	sycl_device.deallocate(gpu_data2);
}

template<typename DataType, typename dev_Selector>
void
sycl_padding_test_per_device(dev_Selector s)
{
	QueueInterface queueInterface(s);
	auto sycl_device = Eigen::SyclDevice(&queueInterface);
	test_simple_padding<DataType, RowMajor, int64_t>(sycl_device);
	test_simple_padding<DataType, ColMajor, int64_t>(sycl_device);
	test_padded_expr<DataType, RowMajor, int64_t>(sycl_device);
	test_padded_expr<DataType, ColMajor, int64_t>(sycl_device);
}
EIGEN_DECLARE_TEST(cxx11_tensor_padding_sycl)
{
	for (const auto& device : Eigen::get_sycl_supported_devices()) {
		CALL_SUBTEST(sycl_padding_test_per_device<float>(device));
	}
}
